QuaPy: A Python-Based Framework for Quantification

TL;DR

QuaPy is an open-source Python framework that provides tools for supervised quantification, including methods, evaluation protocols, datasets, and visualization, to improve prevalence estimation in unlabelled data.

Contribution

It introduces a comprehensive, easy-to-use Python toolkit for quantification, integrating multiple methods, datasets, and evaluation tools for the first time.

Findings

Outperforms standard classify-and-count methods

Includes multiple quantification algorithms and evaluation metrics

Facilitates analysis with visualization tools

Abstract

QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via supervised learning, where a quantifier is a predictor that estimates the relative frequencies (a.k.a. prevalence values) of the classes of interest in a sample of unlabelled data. While quantification can be trivially performed by applying a standard classifier to each unlabelled data item and counting how many data items have been assigned to each class, it has been shown that this "classify and count" method is outperformed by methods specifically designed for quantification. QuaPy provides implementations of a number of baseline methods and advanced quantification methods, of routines for quantification-oriented model selection, of several broadly accepted evaluation measures, and of robust evaluation protocols routinely used in the field. QuaPy also makes available datasets commonly used for testing quantifiers, and offers visualization tools for facilitating the analysis and interpretation of the results. The software is open-source and publicly available under a BSD-3 licence via https://github.com/HLT-ISTI/QuaPy, and can be installed via pip (https://pypi.org/project/QuaPy/)